6 papers
VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
Nina Cai, Jinguang Han, Weizhi Meng
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protecting data privacy. Howev…
A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han +6
This paper introduces a dataset and an experimental study on Decentralized Federated Learning (DFL) for Internet of Things (IoT) crowdsensing malware detection. The dataset compris…
LTRAS: A Linkable Threshold Ring Adaptor Signature Scheme for Efficient and Private Cross-Chain Transactions
Yi Liang, Jinguang Han
Despite the advantages of decentralization and immutability, blockchain technology faces significant scalability and throughput limitations, which has prompted the exploration of o…
Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the pr…
Flexible Threshold Multi-client Functional Encryption for Inner Product in Federated Learning
Ruyuan Zhang, Jinguang Han, Liqun Chen
Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without disclosing their local data. To add…
Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning
Ruyuan Zhang, Jinguang Han
Federated learning (FL) based on cloud servers is a distributed machine learning framework that involves an aggregator and multiple clients, which allows multiple clients to collab…